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检索条件"机构=Science and Technology on Complex System Control and Intelligent Agent Cooperation"

共 366 条记录，以下是221-230 订阅

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Optimization of Missile Path Planning Based on APMO-HV Algorithm

Optimization of Missile Path Planning Based on APMO-HV Algor...

引用

2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Zhang, Hu Wang, Shuai Liu, Tonglin Zhou, Aimin Zhang, Yi Beijing Electro-mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Changzhou University School of Mechanical Engineering Changzhou China East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology Shanghai China

ISBN: (纸本)9781728137926

In this paper, with considerations of low efficiency of missile path planning (MPP) by traditional aggregation technology, it uses affinity propagation based multi-objective evolutionary algorithm with hypervolume environment selection (APMO-HV) to solve the problem of MPP after establishing the MPP model. The experimental part compares and analyzes APMO-HV with six state-of-the-art algorithms, and applies it to address the MPP problem. The experimental results show that compared with the other six algorithms, APMO-HV has achieved the best solution performance in both the GLT test suite and MPP problem. This not only validates the effect of the proposed algorithm, but also enriches and improves the research results of MPP. © 2019 IEEE.

关键词： Evolutionary algorithms

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Aircraft Autonomous Positioning Based on a Coarse to Fine Multi-modal Image Registration Scheme

Aircraft Autonomous Positioning Based on a Coarse to Fine Mu...

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2021 IEEE International Conference on Power Electronics, Computer Applications, ICPECA 2021

作者： Sun, Fangde Fu, Wenxing Lu, Xiaohui Dong, Jing Han, Songlai Central South University School of Aeronautics and Astronautics Changsha China Science and Technology on Complex System Control Intelligent Agent Cooperation Laboratory Beijing China People's Liberation Army 91039th Unit Beijing China National University of Defense Technology Aerospace Science and Engineering Changsha China

ISBN: (纸本)9781728190037

In this paper, an autonomous positioning method based on coarse-to-fine multi-modal image matching is proposed for UAV navigation in GPS denied environment. Coarse image matching refers to roughly determining the approximate position of the real-time image in the reference image. Fine image matching refers to iterative matching around the position obtained by coarse image matching to achieve more accurate matching results. Besides, RANSAC is adopted to eliminate the outliers of image matching. After getting the result of image matching, we use PNP to estimate the position of the aircraft. The proposed position method has the advantages of high accuracy and reliability. The experimental results show that the average error of the position of the proposed method is 59%. © 2021 IEEE.

关键词： Image matching

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A transfer learning fusion model for semi-physical simulation and filed accuracy data 3

A transfer learning fusion model for semi-physical simulatio...

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3rd International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2022

作者： Geng, Huapin Li, Xiaolu Li, Ziwei Suo, Bin Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing100083 China Beijing Electro-mechanical Engineering Institute Beijing100083 China School of Information Engineering Southwest University of Science and Technology Mianyang621010 China

ISBN: (纸本)9783800759330

In order to solve the problem that the accuracy assessment of a single data source is subject to multiple constraints, data fusion reliability evaluation model based on transfer learning was established. Take the high-confidence but small sample field test data as the target domain, the semi-simulation test data as the source domain. Using the principal component analysis to extract the features separately, then embedding the feature distribution in glassman manifold space as two points, construct geodesic flow curve to attach them. So as to achieve data fusion, map the source and target domain to a common space through the geodesic flow curve. Accuracy evaluation can be calculated by constructing confidence region of semi-simulation data. The example verifies the feasibility and effectiveness of this model. © VDE VERLAG GMBH.

关键词： Data fusion

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Cross-cycle iterative unmanned aerial vehicle reentry guidance based on reinforcement learning

Cross-cycle iterative unmanned aerial vehicle reentry guidan...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Yang Cheng Zunshi Shui Cheng Xu Tianyu Feng Yiyang Jiang Beijing Aerospace Technology Research Institute Beijing China Science and technology on complex system control and intelligent agent cooperation laboratory Beijing China

The traditional predictive correction algorithm requires a large number of iterative calculations for the predicted trajectory, which greatly occupies a large amount of computing resources, so that the real-time solution of the guidance command can not be guaranteed, and the guidance accuracy will have a large impact. And the prediction correction guidance requires the algorithm to have the ability of selfadaptation and intelligent learning. Therefore, this paper proposes a cross-cycle iterative hypersonic UAV predictive correction guidance method based on reinforcement learning. The parametric control variable (CVP) method is used to construct the parametric model of the guidance command. The actor-critic-based reinforcement learning method is used to solve the guidance command in real time, and the guidance information is effectively transmitted in the adjacent guidance solution cycle. The guidance error converges to within the allowable accuracy range during the cross-cycle iteration. Monte Carlo simulation shows that the proposed method has good adaptability to initial conditions and flight parameter uncertainty, and can guarantee the real-time performance of the guidance command while achieving high-precision guidance.

关键词： Trajectory Aircraft Atmospheric modeling Earth Mathematical model Iterative algorithms Prediction algorithms

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A belief rule-based fault diagnosis method for Multi-UAVs under missing attribute data

A belief rule-based fault diagnosis method for Multi-UAVs un...

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2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Zhao, ErKai Hao, MingRui Yang, RuoHan Yue, KeYuan Yu, DeLin Harbin Normal University School of Computer Science and Information Engineering Harbin China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Northwestern Polytechnical University School of Electronics and Information Xi'an China Northwestern Polytechnical University School of Mathematics and Statistics Xi'an China

ISBN: (纸本)9798350312492

To reasonably improve the missing attribute data and effectively integrate sample data and uncertain expert knowledge, this paper proposes a new fault diagnosis method based on a belief rule base (BRB). In the case of missing attribute data, the maximum likelihood estimation (MLE) method is used to complete the missing attribute data, and the fault diagnosis method of the multi-UAVs is constructed by using other complete attribute data as the input information of BRB. Finally, the effectiveness of the proposed method is verified by experimental simulation. © 2023 IEEE.

关键词： Failure analysis

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Warship Object Detection in Remote Sensing Images with Improved YOLOv5

Warship Object Detection in Remote Sensing Images with Impro...

引用

Chinese Automation Congress (CAC)

作者： Caiyuan Liu Yuan Li Linxiu Chen Weili Guan School of Automation Beijing Institute of Technology Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

Aiming at the task of detecting ships on the sea surface in remote sensing images, there are a lot of disturbances such as variable object sizes, cloud occlusion, complex image backgrounds, and different ship orientations, etc. In this paper, we propose a ship rotating object detection network based on the improved YOLOv5, which is constructed through the strategies of introducing Swin Transformer as a feature extraction network to enhance the feature extraction performance of the network, introducing a rotating detection head to realize the detection of the rotation angle, and modifying the network loss function to accelerate the convergence of the network. The network finally achieves a 71.7% map on ShipRSImageNet validation set, which is an improvement of 2.4% compared with the original network model. The network proposed in this paper solves the problem that the YOLOv5 algorithm is unable to detect rotating objects, and the network based on the self-attention mechanism is used to further enhance the ability to detect small objects. Finally, a ship object detector that can be used in real remote sensing satellite images is obtained.

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Relative navigation method for unmanned aerial vehicles formation using consensus unscented Kalman filter

Relative navigation method for unmanned aerial vehicles form...

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International Symposium on Autonomous systems (ISAS)

作者： Zhixing Zhuang Baichun Gong Xin Ding Mingrui Hao Linxiu Chen Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

A relative navigation method in which the measurement information between aircraft only has distance or angle was proposed to achieve accurate relative navigation between aircraft formations in the global navigation satellite system (GNSS) Denied Environment. In addition, four relative measurement schemes are analyzed. An augmented state is introduced in the relative navigation system model, and the observability analysis of the navigation system is carried out relying on the Lie derivative theory, which gives the limitation of motion: the relative position and velocity of the aircraft cannot be parallel or zero, and a single aircraft cannot keep flying straight. A consensus unscented Kalman filter (CUKF) algorithm is established using the geometric characteristics in flight. The observability of the system state is improved after using the additional constraint. Finally, a standard Monte Carlo simulation system is established, and the system simulation is carried out, which shows that the Observability analysis is verified to be correct, and the algorithm demonstrates high accuracy.

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Entity Recognition for Military Situation Awareness Knowledge Graph with Wikipedia Data

Entity Recognition for Military Situation Awareness Knowledg...

引用

Chinese control and Decision Conference, CCDC

作者： Linxiu Chen Weili Guan Xudong Guo Yuan Li Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China School of Automation Beijing Institute of Technology Beijing China

Entity recognition is an essential component of knowledge representation and knowledge extraction research. To enhance military situation awareness through the construction of a knowledge graph, this paper presents a novel method, BERTATT_POSBiLSTMLSTMCRF, which is based on the traditional entity recognition model BERT_BiLSTM_CRF. The local location information and the impact of the entity's position in the sentence on the entity recognition task are both fully considered by introducing the attention mechanism. Additionally, an LSTM layer is added after the BiLSTM layer to deal with long-distance label dependencies while improving the model's ability to recognize long entities. Comparative experiments demonstrate that the improved model proposed in this paper is effective in entity recognition with Wikipedia data.

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SeqEnhance: A Lightweight Image Processing PipeLine for Low-Light Image Enhancement 7

SeqEnhance: A Lightweight Image Processing PipeLine for Low-...

引用

7th Asian Conference on Artificial Intelligence technology, ACAIT 2023

作者： Zhou, Runlin Huang, Peiliang Zhang, Dingwen Ren, Jun Anhui University AHU-IAI Ai Joint Laboratory Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Northwestern Polytechnical University School of Automation Xi'an China Science And Technology On Complex System Control And Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9798350359145

Low-light image enhancement (LLIE) is an important task in computer vision, aiming to improve the visual perception or interpretability of images captured in poorly illuminated environments. Recently, deep learning based methods have been extensively explored to address this issue. While many of these methods have achieved significant advancements in various evaluation metrics for LLIE, only a few have made progress in improving inference speed for the resulting images. As a result, achieving both high-quality enhancements and efficient inference speed remains a challenge. To tackle this challenge, we propose SeqEnhance, a fast LLIE method based on a predefined parameterized image processing pipeline. Our approach combines the inference capabilities of deep neural networks for parameter estimation and the efficient processing capabilities of image processing pipelines to generate enhanced images in an end-to-end manner. The experimental results demonstrate that the proposed method achieves competitive performance on image quality evaluation metrics such as PSNR and SSIM with a fast inference speed. © 2023 IEEE.

关键词： Image enhancement

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A Infrared Cloud Image Simulation Method for Cloud Segmentation Network Training

A Infrared Cloud Image Simulation Method for Cloud Segmentat...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Hang Qi Jianquan Yuan Lei Li Jun Ren Jie Liang Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Institute of Mechanical and Electrical Engineering Beijing China

ISBN: (数字)9780996007894

ISBN: (纸本)9781728146331

Due to the difficulty of acquiring real aerial infrared cloud images, we propose an efficient way to set up cloud simulation data set by particle system and texture synthesis, in order to alleviate the problem of data shortage. The deep network for cloud segmentation is trained on our simulation image data set, substantially decreasing the demand for real aerial cloud images. When testing on the real image data set, a satisfactory segmentation result with 79.64% has been reached which can meet the usage requirements. Our method can make the deep cloud segmentation network training feasible despite of the real image shortage, reducing the cost of data acquisition.

关键词： Clouds Image segmentation Data models Training Shape Image edge detection Force

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